/**
* A wrapper for sampling one tree during the Gibbs sampler
*
* @param sample_num The current sample number of the Gibbs sampler
* @param t The current tree to be sampled
* @param trees The trees in this Gibbs sampler
* @param tree_array_illustration The tree array (for debugging purposes only)
* @return The responses minus the sum of the trees' contribution up to this point
*/
protected double[] SampleTree(
int sample_num,
int t,
CGMBARTTreeNode[] trees,
TreeArrayIllustration tree_array_illustration) {
// first copy the tree from the previous gibbs position
final CGMBARTTreeNode copy_of_old_jth_tree =
gibbs_samples_of_cgm_trees[sample_num - 1][t].clone();
// okay so first we need to get "y" that this tree sees. This is defined as R_j in formula 12 on
// p274
// just go to sum_residual_vec and subtract it from y_trans
double[] R_j =
Tools.add_arrays(
Tools.subtract_arrays(y_trans, sum_resids_vec), copy_of_old_jth_tree.yhats);
// now, (important!) set the R_j's as this tree's data.
copy_of_old_jth_tree.updateWithNewResponsesRecursively(R_j);
// sample from T_j | R_j, \sigma
// now we will run one M-H step on this tree with the y as the R_j
CGMBARTTreeNode new_jth_tree =
metroHastingsPosteriorTreeSpaceIteration(
copy_of_old_jth_tree, t, accept_reject_mh, accept_reject_mh_steps);
// add it to the vector of current sample's trees
trees[t] = new_jth_tree;
// now set the new trees in the gibbs sample pantheon
gibbs_samples_of_cgm_trees[sample_num] = trees;
tree_array_illustration.AddTree(new_jth_tree);
// return the updated residuals
return R_j;
}
/**
* A wrapper for sampling the mus (mean predictions at terminal nodes). This function implements
* part of the "residual diffing" explained in the paper.
*
* @param sample_num The current sample number of the Gibbs sampler
* @param t The tree index number in 1...<code>num_trees</code>
* @see Section 3.1 of Kapelner, A and Bleich, J. bartMachine: A Powerful Tool for Machine
* Learning in R. ArXiv e-prints, 2013
*/
protected void SampleMusWrapper(int sample_num, int t) {
CGMBARTTreeNode previous_tree = gibbs_samples_of_cgm_trees[sample_num - 1][t];
// subtract out previous tree's yhats
sum_resids_vec = Tools.subtract_arrays(sum_resids_vec, previous_tree.yhats);
CGMBARTTreeNode tree = gibbs_samples_of_cgm_trees[sample_num][t];
double current_sigsq = gibbs_samples_of_sigsq[sample_num - 1];
assignLeafValsBySamplingFromPosteriorMeanAndSigsqAndUpdateYhats(tree, current_sigsq);
// after mus are sampled, we need to update the sum_resids_vec
// add in current tree's yhats
sum_resids_vec = Tools.add_arrays(sum_resids_vec, tree.yhats);
}
